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October 15, 2025Nature CommunicationsOpen Access

LINS: A general medical Q&A framework for enhancing the quality and credibility of LLM-generated responses

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Authors

SWSheng WangFZFangyuan ZhaoDBDan Bu

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Overview

Evaluation shows LINS improves medical knowledge accuracy in large language models, indicating higher credibility and user trust.

Key Points

  • LINS significantly enhances the quality and credibility of large language model outputs in clinical scenarios.
  • Evaluation against 15,530 questions showed 87% of physicians found it useful in evidence-based medical situations.
  • This framework effectively integrates up-to-date medical knowledge to improve response validity and specificity.
  • Findings indicate that LINS can potentially transform large language models into trustworthy clinical assistants.

Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/68efbd16d61273c8652d7f23https://doi.org/10.1038/s41467-025-64142-2
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